---
title: "COVID-19 Effects on the US Labor Market"
author: "Ruan Almeida"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    # social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data(coronavirus)
# View(coronavirus)
# max(coronavirus$date)

`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(country == "US") %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

df_daily <- coronavirus %>%
  dplyr::filter(country == "US") %>%
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  #dplyr::mutate(active = confirmed - death - recovered) %>%
  dplyr::mutate(active = confirmed - death) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    # recovered_cum = cumsum(recovered),
    active_cum = cumsum(active)
  )


df1 <- coronavirus %>% dplyr::filter(date == max(date))
```

COVID-19
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
  caption = "Total confirmed cases",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```
















### death {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Death cases (death rate)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


Row
-----------------------------------------------------------------------

### **Daily cumulative cases by type** (US only)
    
```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(
    x = ~date,
    # y = ~active_cum,
    y = ~confirmed_cum,
    type = "scatter",
    mode = "lines+markers",
    # name = "Active",
    name = "Confirmed",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Death",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  # plotly::add_annotations(
  #   x = as.Date("2020-02-04"),
  #   y = 1,
  #   text = paste("First case"),
  #   xref = "x",
  #   yref = "y",
  #   arrowhead = 5,
  #   arrowhead = 3,
  #   arrowsize = 1,
  #   showarrow = TRUE,
  #   ax = -10,
  #   ay = -90
  # ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-11"),
    y = 3,
    text = paste("First death"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -90,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-18"),
    y = 14,
    text = paste(
      "Lockdown"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Cumulative number of cases"),
    xaxis = list(title = "Date"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

Labor Market 
=======================================================================

```{r BLSrates}

library(blscrapeR)
library(ggplot2)

#Key Install intructions
#library(blscrapeR)
#set_bls_key("YOUR_KEY_IN_QUOTATIONS")
# First time, reload your enviornment so you can use the key without restarting R.
#readRenviron("~/.Renviron")
# You can check it with:
#Sys.getenv("BLS_KEY")

# Extracting different datasets from BLS, using my personal access ID
# NOTE on series IDs: 
# UNEMPLOYMENT RATE - Civilian labor force - LNS14000000
# JOB OPENINGS RATE, Total nonfarm - JTU000000000000000JOR
df_rates <- bls_api(c("LNS14000000", "JTU000000000000000JOR"),
              startyear = 2019, endyear = 2020, Sys.getenv("98768e2ab6474836a2583fbbca4e7d75")) %>%
  # Add time-series dates
  dateCast()
```


Column {data-width=400}
-------------------------------------


### **US Unemployment Rate (civilian labor force) dashboard**
    

```{r unemployment}
# Slicing the dataset with data only related to unemployment rate
ggunemployment <- subset(df_rates, seriesID=="LNS14000000")

# Plotting the data
ggunemployment %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~value,
    type = "scatter",
    mode = "lines+markers",
    line = list(color = 'red'),
    name = "UNEMPLOYMENT"
  ) %>%

  plotly::layout(
    title = "",
    legend = list(x = 0.7, y = 0.9),
    yaxis = list(title = "Rate"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'red'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )

#library(ggplot2)
#ggplot(ggunemployment, aes(x=date, y=value)) +
#  geom_line() +
#  labs(title = "UNEMPLOYMENT (RATE)")

```
 
### **US Job Openings Rate (total nonfarm) dashboard**

```{r job}

# Slicing the dataset with data only related to job openings rate
ggjobs <- subset(df_rates, seriesID=="JTU000000000000000JOR")

# Plotting the data
ggjobs %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~value,
    type = "scatter",
    mode = "lines+markers",
    line = list(color = 'forestgreen'),
    name = "JOB OPENING"
  ) %>%

  plotly::layout(
    title = "",
    legend = list(x = 0.7, y = 0.9),
    yaxis = list(title = "Rate"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'green'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )

#library(ggplot2)
#ggplot(ggjobs, aes(x=date, y=value)) +
#  geom_line() +
#  labs(title = "JOB OPENINGS (RATE)")

```


Maps
=======================================================================
Column {data-width=400}
-------------------------------------

### **US map of unemployment**

```{r unemploymentmap, echo=FALSE}

library(blscrapeR)
# Grap the data in a pre-formatted data frame.
# If no argument is passed to the function it will load the most recent month's data.
df_map <- get_bls_state()

#Use map function with arguments.
map_bls(map_data = df_map, fill_rate = "unemployed_rate", 
               labtitle = "Unemployment Rate by State")
```

### **US map of labor force**

```{r jobsmap, echo=FALSE}

library(blscrapeR)
# Grap the data in a pre-formatted data frame.
# If no argument is passed to the function it will load the most recent month's data.
#df_map <- get_bls_state()

#Use map function with arguments.
map_bls(map_data = df_map, fill_rate = "labor_force_rate", 
               labtitle = "Labor Force Rate by State")

```


About
=======================================================================

**COVID-19 Effects on the US Labor Market - The Monitoring App**

This Monitoring Application provides an overview of the situation of the US labor market (unemployment and job opening rates) in face of 2019 Novel Coronavirus COVID-19 (2019-nCoV) pandemic. This dashboard is built with R using the R Makrdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.

**Code**

The code behind this dashboard is available on [GitHub](https://github.com/ruanmurta/Capstone){target="_blank"}.

**Data**

The data and dashboard are dynamically refreshed (what you see is not static data).

The raw data used as input for the dashboards is extracted from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [database](https://github.com/RamiKrispin/coronavirus){target="_blank"} and U.S. Bureau Of Labor Statistics (blsAPI) [database](https://www.bls.gov/developers/api_r.htm){target="_blank"} 

**Information**

More information about this dashboard and my research can be found in this [article](https://www.linkedin.com/in/ruanmurta/).

**Update**

The data is as of `r format(max(coronavirus$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.



* [About Ruan Almeida](https://about.me/ruan_almeida)*.